Small multiples of lineplots > maps (ok, not always, but yes in this case)

– Did not put the data on a map
– Ordered the countries by the most recent data point rather than alphabetically
– Scale labels are found only on outer edge of the chart area, rather than one set per panel
– Only used three labels for the 11 years on the plot
– Did not overdo the vertical scale either

The nicest feature was the XL scale applied only to South Korea. This destroys the small-multiples principle but draws attention to the top left corner, where the designer wants our eyes to go. I would have used smaller fonts throughout.

I agree with all of Kaiser’s comments. I could even add a few more, like using light gray for the backgrounds and a bright blue for the lines, spacing the graphs well, using full country names rather than three-letter abbreviations. There are so many standard mistakes that go into default data displays that it is refreshing to see a simple graph done well.

Kaiser continues:

One way to appreciate the greatness of the chart is to look at alternatives.

A variation is a cartogram in which the physical size and shape of countries are mapped to the underlying data. Here’s one on Worldmapper (link):

One problem with this transformation is what to do with missing data.

Yup. Also, the big big trouble with the transformed map is that the #1 piece of information it gives you is something we all know already—that China has a lot of people. Sure, if you look carefully you can figure out other things—hey, India has a billion people too but it’s really small on the map, I guess nobody’s drinking much there—but that’s all complicated reasoning involving mental division.

To put it another way, if this distorted map works—and it may well “work,” in the sense of grabbing attention and motivating people to look deeper at these data, which is the #1 goal of an infographic—if it does work, it’s doing so using the Chris Rock effect, in which we enjoy the shock of recognition of a familiar idea presented in an unfamiliar way.

I agree that this one is better than the Economist map above. Wikipedia’s uses an equal-area projection (I think) so you don’t get so distracted by massive Greenland, a sensible color scheme with a natural ordering (unlike the Economist’s where it’s obvious that red is highest and pink is next, but then you have to go back to the legend to figure out how the other colors are ordered), also the legend has high numbers on top and low on bottom which again is sensible.

Still and all, the original grid of lines is better for me because (a) it shows the comparisons quantitatively (which in this case makes sense; those differences are huge (actually, so huge that it makes me wonder whether the comparisons are appropriate; is wine drinking in Portugal so much different than downing shots of soju in Korea?)) and, (b) it shows the time trends (most notably, the declines in Russia and Brazil, the increase from a low baseline in India, and Korea’s steady #1 position).

The click-through solution

Let me conclude, as always in this sort of discussion, that displaying patterns in the data is not the only reason for a graph. Another reason is to grab attention. If an unusually-colored map catches people’s eyes, maybe that’s the best way to go. My ideal solution would be click-through: the Economist (or wherever) has the colorful map with instructions to click to see the informative grid of line plots, then you can click again and get a spreadsheet with all the numbers.

19 Comments

Map #2 shows some pretty strong spatial patterns that are not so visible in the map #1. Also, while I don’t reject the use of the mean as a summary stat here, I would doubt that the distribution of alcohol consumption is normally distributed. Actually, I would expect the distributions look very interesting. There are few enough timepoints that Violin/beanplots (with mean lines overlayed) could be used instead of the first chart. Perhaps split the data from each continent into different charts, then also show map #2 using the endpoint data.

See what I wrote above under “The click-through solution.” I think a map is a good start but then I’d like to click through to the lineplot. I think that’s a better solution than trying to cram too much information on to the map.

Data used to make the lineplots is a bit weird. Well, at least quite a bit different than WHO’s data in the Wikipedia map. For example, Euromonitor data: Japan (4.4) vs S-Korea (13.7), WHO: Japan (7-8.99) vs S-Korea (7-8.99)

One explanation could be the used measurment. In the Euromonitor dataset it is shots/week compared to WHO’s pure alchol litres per capita. Shots in S-Korea have lower alcohol content?

Yes, this plot is very distorted: the Koreans’ favorite drink is Soju, which usually has an average alcohol content of 20% ABV. So the comparison of shots/week does not tell the story of alcohol consumption well.

The way I took this data was that it was looking only at consumption of spirits. Since many people don’t drink spirits, these numbers are a bit odd.

Rahul: I don’t think this data is converting to “shot equivalents” for beer and wine and other alcohol drinkers. I would be very surprised if US alcohol consumption was as low as an average of 3.3 “standard drinks” per week.

I think the question whatever to use the map approach (Wikipedia) or multiple lineplots depends what is the story one would like to tell. For example, if the goal is to give overview of the alcohol consumption of the whole world, then map does contain a lot more information that the lineplots. But if we would like to concentrate on some set of countries it’s the opposite and so on.

The power of maps to engage interest is nicely demonstrated by the comments, many of which delve into individual countries and start comparing them and looking for explanations. Ideally I would have both side by side with click-thru from the map to selected line charts, using something like crossfilter.

The clickthrough idea is a good compromise but I plead that we reverse the order. It should not go from map to line charts but the reverse. In my view, the most interesting story in this particular dataset is not related to regions of the world or any kind of spatial pattern. If someone wants to know Korea’s place in the world, then click on the Korea panel and look at the world map. But not the other way round! Maps are totally over-used for geographical data.

K
The first image should be the one that gets the eyeballs. Maybe not a map but some flashy viz. Then #2 are the lineplots. Arguably the map should be #3 as the map serves as a sort of look-up table. But I think so many people prefer maps to lineplots that in practice the map has to come first.